The field of the invention is systems and methods for magnetic resonance imaging (“MRI”). More particularly, the invention relates to systems and methods for reconstructing images from k-space data acquired using accelerated MRI techniques, such as simultaneous multi-slice acquisitions using multiband radio frequency (“RF”) excitation and undersampled phase encoding.
In MRI it is common to acquire data with phase-encoding (in-plane) undersampling in order to reduce the acquisition times. The use of multiple channel receiver coils, which have unique sensitivities in space, along with specialized image reconstruction algorithms (SENSE and GRAPPA) that can fill in the missing data, make this data undersampling and subsequent “speed up” factor in MRI possible. Another way to accelerate image acquisition in MRI is to use multiband (MB) acquisitions, where, instead of undersampling signals to speed up, multiple signals are excited and refocused simultaneously. This can be considered as a slice (through plane) acceleration of MRI data acquisition as multiple images (slices) are acquired at the same time. In order to disambiguate the MRI signals from the different slices, as with undersampling, multiple channel receiver coils with unique sensitivities in space, along with specialized image reconstruction techniques are needed. Undersampling-based acceleration and simultaneous multi-slice acquisition-based acceleration can be used in conjunction to reduce the time it takes to generate a single image and the time it takes to acquire multiple images.
Different techniques have been proposed on how to disentangle the signals that are acquired at the same time from multiple slice locations. The original proposal was a SENSE-based algorithm. SENSE algorithms are known to be mathematically accurate, but challenging and unstable to implement due to a requirement for accurate sensitivity profiles. To circumvent this limitation, the use of data-driven data-interpolation schemes has been developed. For instance, in CAIPIRINHA a GRAPPA-type interpolation algorithm was used to separate the simultaneously acquired signals.
A similar data-driven approach was exploited and extended for fMRI by S. Moeller, et al., in “fMRI with 16 Fold Reduction Using Multibanded Multislice Sampling,” Proc. of the ISMRM, 2008; page 2366. The principle is that an initial GRAPPA algorithm is deployed that separates the simultaneous signals (images) from the phase-encoding undersampled data, resulting in images (slice) with only undersampling (missing data). To separate out the signals, the reconstructed signals are treated as originating from an extended field of view (FOV), and the use of fast Fourier transforms (FFT) are required. A slice-unaliasing algorithm based on a conventional GRAPPA algorithm was introduced by S. Moeller, et al., in “Multiband multislice GE-EPI at 7 Tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI,” Magn Reson Med, 2010; 63(5):1144-1153. The GRAPPA kernel was calculated from an artificially imposed extended FOV.
In Setsompop et al it was shown that this approach was not compatible with the blipped gradient train pattern, and a different coil-by-coil data driven technique was proposed. A slice-GRAPPA algorithm that performed sensitivity calibration and signal separation directly in the measured (acquired) space, thereby alleviating the need for FFTs, was introduced by K. Setsompop, et al., in “Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty,” Magn Reson Med, 2012; 67(5):1210-1224. This method also has the additional benefit that it is compatible with the relative shift between slices that are introduced with the Blipped-CAIPIRINHA method.
For both the slice-GRAPPA method and the method described by S. Moeller, et al., the use of both slice-aliasing (simultaneous acquisitions) and phase-encoding undersampling is corrected by the sequential application of either GRAPPA, and then a slice-separation algorithm, or in the reverse order (i.e. first a slice-separation and then a correction with GRAPPA). These approaches work well for data with high signal to noise ratios (SNR), and have been thoroughly tested for both functional MRI and diffusion data. However, for low SNR data, which is common in diffusion MRI, high spatial resolution MRI, and also in other quantitative and dynamic imaging techniques in MRI, these algorithms are not sufficient.
Regardless of whether the slice-GRAPPA method or the method described by S. Moeller, et al., is used, the relative phase difference between aliased slices is used in the reconstruction, since both magnitude and phase of individual coil sensitivities are used for unaliasing. The relative phase difference, however, can change during successive acquisitions. For instance, in an fMRI time series, or in subsequent acquisitions of multiple diffusion directions, the changing relative phase differences result in temporal fluctuations in signal intensity, which increases the variability in fMRI and in the detection of neuronal fibers in diffusion imaging.
It would therefore be desirable to provide a method for multiband calibration in simultaneous multi-slice imaging techniques that properly accounts for the relative phase between multiband slices.